CN109064043B - Evaluation method, evaluation device, computing equipment and storage medium - Google Patents
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Abstract
The present specification provides an evaluation method, an evaluation apparatus, a computing device, and a storage medium, wherein the evaluation method includes: acquiring user quantities of a plurality of first schemes and a plurality of second schemes of an evaluation object in an observation period; converting the user quantities of the plurality of first schemes and the user quantities of the plurality of second schemes according to a set conversion mode to obtain a first data distribution and a second data distribution; calculating a first distance between the first data distribution and the second data distribution; determining a first position proportion of the first distance in a set number of first sample value reference distances, determining a second position proportion of the first distance in a set number of second sample value reference distances, and respectively determining a neutral selection degree of a first scheme and a neutral selection degree of a second scheme of the evaluation object according to the first position proportion and the second position proportion; and evaluating the first scheme and the second scheme based on the degree of selection of the first scheme and the degree of selection of the second scheme.
Description
Technical Field
The present disclosure relates to the field of solution evaluation technologies, and in particular, to an evaluation method, an evaluation apparatus, a computing device, and a storage medium.
Background
In iterative improvement of products, companies often encounter two sets of schemes and do not know that better results can be obtained by selecting the schemes, and when the problems are encountered, the companies often divide users into two groups randomly through a user random selector, and then compare the use effects of the two groups of users. Therefore, by using a large number of random users, companies can truly compare the quality of the two sets of schemes, the method has a problem in use, and when the dimensionality is excessive, slight changes can cause great influence on the result.
Disclosure of Invention
In view of this, embodiments of the present disclosure provide an evaluation method, an evaluation apparatus, a computing device, and a storage medium, so as to solve technical defects in the prior art.
According to a first aspect of embodiments herein, there is provided an evaluation method including:
acquiring user quantities of a plurality of first schemes and a plurality of second schemes of an evaluation object in an observation period;
converting the user quantities of the plurality of first schemes and the user quantities of the plurality of second schemes according to a set conversion mode to obtain a first data distribution and a second data distribution;
calculating a first distance between the first data distribution and the second data distribution;
determining a first position proportion of the first distance in a set number of first sample value reference distances, determining a second position proportion of the first distance in a set number of second sample value reference distances, and respectively determining a neutral selection degree of a first scheme and a neutral selection degree of a second scheme of the evaluation object according to the first position proportion and the second position proportion;
and evaluating the first scheme and the second scheme based on the degree of selection of the first scheme and the degree of selection of the second scheme.
Optionally, the obtaining the user quantities of the plurality of first schemes and the plurality of second schemes of the evaluation object in the observation period includes:
and acquiring the user quantity of the plurality of first schemes and the user quantity of the plurality of second schemes of the evaluation object in the observation period from the log file.
Optionally, the set conversion mode is a ratio of the daily user amount to the total user amount in the observation period.
Optionally, the set number of first sample value reference distances and the set number of second sample value reference distances, and the degree of selection in the first scheme and the degree of selection in the second scheme are obtained as follows:
generating a set number of sample distributions in a random manner;
calculating the distance between each sample distribution and the first data distribution to obtain a set number of first sample value reference distances, and calculating the distance between each sample distribution and the second data distribution to obtain a set number of second sample value reference distances;
and respectively determining the degree of selection of the first scheme and the degree of selection of the second scheme of the evaluation object according to the first position proportion and the second position proportion.
Optionally, the randomly generating the set number of sample distributions includes:
a set number of sample distributions are generated by hadoop clustering.
Optionally, the evaluating the first scheme and the second scheme based on the degree of selection of the first scheme and the degree of selection of the second scheme includes:
determining a medium-selectivity standard value according to an evaluation object;
and comparing the degrees of intermediate selection of the first scheme and the second scheme with the standard value of the degrees of intermediate selection, wherein the scheme closer to the standard value of the degrees of intermediate selection is the scheme of intermediate selection.
According to a second aspect of embodiments herein, there is provided an evaluation apparatus comprising:
an acquisition module: the evaluation object is configured to acquire a plurality of first plan user amounts and a plurality of second plan user amounts in an observation period.
A conversion module: the data distribution conversion method is configured to convert the user quantity of the plurality of first schemes and the user quantity of the plurality of second schemes according to a set conversion mode to obtain a first data distribution and a second data distribution.
A calculation module: is configured to calculate a first distance between the first data distribution and the second data distribution.
A determination module: the method comprises the steps of determining a first position proportion of the first distance in a set number of first sample value reference distances, determining a second position proportion of the first distance in a set number of second sample value reference distances, and determining a degree of selection of a first scheme and a degree of selection of a second scheme of the evaluation object according to the first position proportion and the second position proportion respectively.
An evaluation module: configured to evaluate the first and second solutions based on the degree of selection of the first solution and the degree of selection of the second solution quantity.
Optionally, the obtaining module is further configured to:
and acquiring the user quantity of the plurality of first schemes and the user quantity of the plurality of second schemes of the evaluation object in the observation period from the log file.
Optionally, the conversion module is a total ratio of the daily user amount to the observation period user amount.
Optionally, the determining module includes:
a first determination unit: configured to generate a set number of sample distributions in a random manner;
a second determination unit: configured to calculate a distance between each sample distribution and the first data distribution resulting in a set number of first sample value reference distances, and to calculate a distance between each sample distribution and the second data distribution resulting in a set number of second sample value reference distances;
a third determination unit: configured to take the position proportion as the degree of selection of the first scheme and the degree of selection of the second scheme quantity of the evaluation object.
Optionally, the first determining unit is further configured to:
a set number of sample distributions are generated by hadoop clustering.
Optionally, the evaluation module comprises:
a fourth evaluation unit: configured to determine a medium-selectivity standard value according to an evaluation object;
a fifth evaluation unit: and comparing the degrees of intermediate selection of the first scheme and the second scheme with the standard value of the degrees of intermediate selection, wherein the scheme closer to the standard value of the degrees of intermediate selection is the scheme of intermediate selection.
According to a third aspect of embodiments herein, there is provided a computing device comprising a memory, a processor and computer instructions stored on the memory and executable on the processor, the processor implementing the steps of the evaluation method when executing the instructions.
According to a fourth aspect of embodiments herein, there is provided a computer readable storage medium storing computer instructions which, when executed by a processor, implement the steps of the evaluation method.
In the embodiment of the present specification, two different schemes are selected in an observation period, a first data distribution and a second data distribution are obtained by converting a conversion manner set for the two schemes respectively according to a user amount of a first scheme and a user amount of a second scheme, a distance between the first data distribution and the second data distribution is calculated according to a distance calculation formula, a reference distance between the first data distribution and a first sample value and a reference distance between the second data distribution and a second sample value are calculated according to the calculation formula, distances of a set number of random sample groups are obtained, a position ratio of the distances of the first data distribution and the second data distribution in the set number of sample reference distances is found, a neutral degree of the two schemes is determined according to the position ratio respectively, and the obtained neutral degree of the two schemes is compared with a neutral degree of a standard value, and taking the scheme with the similar degree of selection in the standard values as a selection scheme.
Drawings
FIG. 1 is a block diagram of a computing device provided by an embodiment of the present application;
FIG. 2 is a flow chart of an evaluation method provided by an embodiment of the present application;
FIG. 3 is a flow chart of an evaluation method provided by an embodiment of the present application;
fig. 4 is a block diagram of an evaluation apparatus provided in an embodiment of the present application.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present description. This description may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, as those skilled in the art will be able to make and use the present disclosure without departing from the spirit and scope of the present disclosure.
Fig. 1 is a block diagram illustrating a configuration of a computing device 100 according to an embodiment of the present specification. The components of the computing device 100 include, but are not limited to, memory 110 and processor 120. The processor 120 is coupled to the memory 110 via a bus 130 and the database 150 is used to store user data.
Computing device 100 also includes access device 140, access device 140 enabling computing device 100 to communicate via one or more networks 160. Examples of such networks include the Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the internet. Access device 140 may include one or more of any type of network interface (e.g., a Network Interface Card (NIC)) whether wired or wireless, such as an IEEE802.11 Wireless Local Area Network (WLAN) wireless interface, a worldwide interoperability for microwave access (Wi-MAX) interface, an ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a bluetooth interface, a Near Field Communication (NFC) interface, and so forth.
In one embodiment of the present description, the other components of the computing device 100 described above and not shown in FIG. 1 may also be connected to each other, such as by a bus. It should be understood that the block diagram of the computing device architecture shown in FIG. 1 is for purposes of example only and is not limiting as to the scope of the description. Those skilled in the art may add or replace other components as desired.
Computing device 100 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., tablet, personal digital assistant, laptop, notebook, netbook, etc.), a mobile phone (e.g., smartphone), a wearable computing device (e.g., smartwatch, smartglasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or PC. Computing device 100 may also be a mobile or stationary server.
Wherein the processor 120 may perform the steps of the evaluation method shown in fig. 2. Fig. 2 is a flow chart illustrating an evaluation method according to an embodiment of the present description, including step 202 through step 210.
Fig. 2 shows a flowchart of an evaluation method in the embodiment of the present invention, as shown in fig. 2, including:
step 202: and acquiring the user quantity of a plurality of first schemes and the user quantity of a plurality of second schemes of the evaluation object in the observation period.
In an embodiment of the present specification, the evaluation object may be an application program or a web page.
The observation period is the observation time of the evaluation object, and the user amount of the first scheme and the user amount of the second scheme are the user usage amount of the two schemes of the evaluation object in the observation period.
For example, two schemes of a certain application are taken as evaluation objects, and if 1000 preset users are provided, the two groups of users are divided into two groups and each group comprises 500 users, the two groups of users use the certain application of the two design schemes, the first scheme comprises 300 users, the second scheme comprises 200 users, the number of users of the first scheme is 300, and the number of users of the second scheme is 200.
In an embodiment of the present specification, the acquiring the user quantities of the plurality of first solutions and the plurality of second solutions of the evaluation target during the observation period includes:
and acquiring the user quantity of the plurality of first schemes and the user quantity of the plurality of second schemes of the evaluation object in the observation period from the log file.
Network equipment, a system, a service program and the like generate an event record called log when in operation, and the event record is a log file of an evaluation object. Each row of the log in the log file of the evaluation object records the description of the related operation such as date, time, user and action.
For example, in an embodiment of the present specification, if the observation period is 30 days, and users who have visited the group a for 1 day within 30 days are taken as the user volume of the first plan, and users who have visited the group B for 1 day within 30 days are taken as the user volume of the second plan, the user volume of the first plan and the user volume of the second plan may be obtained from a log file of the evaluation object.
Counting the use conditions of the two schemes in log files of the two schemes of the evaluation object, wherein the user quantity of the first scheme on the 1 st day is 400 and the user quantity of the second scheme on the 1 st day is 450 through counting; the first plan user amount on day 2 is 300, the second plan user amount on day 2 is 350; by analogy, all the first scheme user amount and the second scheme user amount of 30 days are obtained.
Step 204: and converting the user quantities of the plurality of first schemes and the user quantities of the plurality of second schemes according to a set conversion mode to obtain a first data distribution and a second data distribution.
In an embodiment of the present specification, the set conversion manner is a ratio of the daily user amount to the total user amount during the observation period.
For example, in an embodiment of the present disclosure, comparing the advantages and disadvantages of the first scheme and the second scheme, the observation period is 30 days, 1000 users are divided into two groups a.b, the group a has 500 users, the group B has 500 users, two design schemes of a web page are used, the group a users use the first scheme, the group B users use the second scheme, the proportion of the users in 1 day of the group a users in 30 days is 3.1%, and the proportion of the users in 1 day of the group B users in 30 days is 3.3%; the user quantity accounting ratio of 2 days in 30 days of the group A users is 4.1%, the user quantity accounting ratio of 2 days in 30 days of the group B users is 4.3%, and the like, the user quantity accounting ratios of other days are obtained, and the obtained two groups of data distribution are the data distribution of the first scheme and the data distribution of the second scheme.
Step 206: a first distance between the first data distribution and the second data distribution is calculated.
In an embodiment of the present specification, the first distance is a Wasserstein distance between a data distribution of the first scheme and a data distribution of the second scheme.
The Wasserstein distance, also called Earth-Mover distance (EM distance), is used to measure the distance between two distributions, and the distance formula is:
in an embodiment of the present invention, in the present specification,
which represents a first distribution of the data,
representing a second data distribution, x representing one data in the first data distribution, y representing one data in the second data distribution, E representing an expected value, and γ representing a joint distribution.
Step 208: determining a first position proportion of the first distance in a set number of first sample value reference distances, determining a second position proportion of the first distance in a set number of second sample value reference distances, and respectively determining a neutral selection degree of a first scheme and a neutral selection degree of a second scheme of the evaluation object according to the first position proportion and the second position proportion.
In an embodiment of this specification, the set number of reference distances of the first sample value and the set number of reference distances of the second sample value are obtained as follows:
generating a set number of sample distributions in a random manner;
calculating the distance between each sample distribution and the first data distribution to obtain a set number of first sample value reference distances, and calculating the distance between each sample distribution and the second data distribution to obtain a set number of second sample value reference distances;
and respectively determining the degree of selection of the first scheme and the degree of selection of the second scheme of the evaluation object according to the first position proportion and the second position proportion.
For example, in an embodiment of the present specification, taking a set number of 1 million as an example, randomly generating ten million sample distribution values, each sample distribution including 30 sample distribution values, and then calculating a Wasserstein distance between each sample distribution and the a.b two-set data distribution, so that we can obtain ten million Wasserstein distances from the a-set data distribution, that is, a first sample value reference distance; ten million Wasserstein distances from the data distribution of the B group can also be obtained, namely the reference distance of the second sample value.
In one embodiment of the present disclosure, a set number of sample distributions may be generated by hadoop clustering.
The hadoop clustering mode belongs to a programming model package, and is a distributed computing platform which can be easily constructed and used by a user, and the user can easily develop and run an application program for processing mass data on hadoop.
In an embodiment of the present specification, the number of the set number generated by the hadoop cluster is ten million, and each sample distribution includes 30 sample distribution values.
And calculating reference distances of sample values of a set number, calculating a second position proportion of the second distance in the reference distance of the second sample value by comparing the first position proportion of the first distance in the reference distance of the first sample value, and determining the intermediate selection degree of the two schemes according to the position proportions.
For example, if the first distance is located at the first million distance from the reference distances of the million sample values, and is the degree of selection of the first scheme, and is arranged at 10% of the reference distance of the million samples, the degree of selection of the first scheme is considered to reach 90% of random distribution, and the degree of selection of the first scheme is 0.9.
If the first distance is located at the position of the first half million of the million sample value reference distances, is the degree of selection of the second scheme, and is arranged at 15% of the million sample reference distances, the degree of selection of the second scheme is considered to reach 85% of random distribution, and the degree of selection of the second scheme is 0.85.
Step 210: and evaluating the first scheme and the second scheme based on the degree of selection of the first scheme and the degree of selection of the second scheme.
In an embodiment of the present specification, the evaluating the first scheme and the second scheme based on the degree of selection of the first scheme and the degree of selection of the second scheme includes:
determining a medium-selectivity standard value according to an evaluation object;
and comparing the degrees of intermediate selection of the first scheme and the second scheme with the standard value of the degrees of intermediate selection, wherein the scheme closer to the standard value of the degrees of intermediate selection is the scheme of intermediate selection.
Determining a selection standard value in the evaluation object according to the service requirement; the selection degree of the first scheme and the selection degree of the second scheme are compared with the selection degree standard value, the scheme which is closer to the selection degree standard value is the target file, and a good effect can be obtained when the scheme which is closer to the selection degree standard value is used.
According to the evaluation method provided by the embodiment of the invention, a large number of random samples are generated by utilizing the hadoop cluster, and the two schemes are evaluated based on the Wasserstein distance, so that the two schemes can be selected based on a large amount of statistical data, and the stability of the correlation calculation value can be improved.
FIG. 3 illustrates an evaluation method of one embodiment of the present description. The evaluation method is described by taking two design scheme evaluations of a webpage as an example, and comprises the following steps:
step 302: and acquiring the user access amount of the webpage A.
Step 304: and acquiring the user access amount of the webpage B.
Step 306: and respectively converting the user access amount of the webpage A and the user access amount of the webpage B according to the ratio of the user amount per day to the total user amount in the observation period to respectively obtain a first data distribution and a second data distribution.
For example, in an observation period of 30 days, the user access amount of the webpage A and the user access amount of the webpage B account for the proportion of the total user access amount in 30 days; the user ratio of 1 day visited by the group A users in 30 days is 3.1%, the user ratio of 1 day visited by the group B users in 30 days is 3.3%, the user amount ratio of 2 days in 30 days of the group A users is 4.1%, the user amount ratio of 2 days in 30 days of the group B users is 4.3%, and the like, the user amount ratios of other days are obtained, and the obtained two groups of data distribution are that the data distribution of the group A is the first data distribution and the data distribution of the group B is the second data distribution.
Step 308: ten million sample distribution values are generated in a hadoop clustering mode.
Step 310: and calculating Wasserstein distances between the first data distribution and the second data distribution, and calculating the Wasserstein distances between the first data distribution and the sample distribution values and between the second data distribution and the sample distribution values respectively.
In an embodiment of the present specification, a Wasserstein distance between the first data distribution and the second data distribution is taken as the first distance.
In an embodiment of the present specification, calculating the Wasserstein distance between the first data distribution and each sample generates ten million first sample value reference distances, and calculating the Wasserstein distance between the second data distribution and each sample generates ten million second sample value reference distances.
Step 312: determining a first position proportion of the first distance in ten million first sample value reference distances, determining a second position proportion of the first distance in ten million second sample value reference distances, and respectively determining the selection degree of the webpage A scheme and the selection degree of the webpage B scheme of the evaluation object according to the first position proportion and the second position proportion.
The obtained degree of selection is obtained by comparing the positions of the first distance in the reference distances of the ten million sample values, in this embodiment, the first distance is located at the position of the first million distance in the reference distances of the ten million sample values, and is the degree of selection of the first scheme, and is arranged at 10% of the reference distances of the ten million samples, and then the degree of selection of the first scheme is considered to reach 90% of random distribution, and then the degree of selection of the first scheme is 0.9.
The first distance is located at the position of the first half million distances from the million sample value reference distances, is the degree of selection of the second scheme, and is arranged at 15% of the reference distance of the million samples, and the degree of selection of the second scheme is considered to reach 85% of random distribution, and is 0.85.
Step 314: and comparing the selection degree of the webpage A scheme and the selection degree of the webpage B scheme with the selection degree standard value, and determining the scheme closer to the selection degree standard value as the selection scheme.
For example, in this embodiment, the selection degree standard value is 1.00, the selection degree of the web page a solution is 0.9, and the selection degree of the web page B solution is 0.85, that is, the selection degree of the web page a solution is closer to the selection degree standard value, and the web page a solution is selected as the selection solution.
The number of observation period days can be determined according to the service demand, and the number is not limited in the application.
In an embodiment of the description, data extraction is performed on the user quantity of the first scheme and the user quantity of the second scheme in the observation period to obtain the user quantity of the access webpage in each day, the user quantity of each day is converted according to the ratio of the user quantity of each day to the total user quantity in the observation period to obtain the data distribution of the first scheme and the data distribution of the second scheme, the Wasserstein distance between the user quantity of the first scheme and the user quantity of the second scheme is calculated, and the selection degree of the first scheme and the second scheme is determined based on the Wasserstein distance to improve the stability of evaluation.
Corresponding to the above method embodiments, the present specification also provides evaluation device embodiments. FIG. 4 shows a block diagram of an evaluation device according to one embodiment of the present disclosure. As shown in fig. 4, the apparatus 400 includes:
the acquisition module 402: the evaluation object is configured to acquire a plurality of first plan user amounts and a plurality of second plan user amounts in an observation period.
The conversion module 404: the data distribution conversion method is configured to convert the user quantity of the plurality of first schemes and the user quantity of the plurality of second schemes according to a set conversion mode to obtain a first data distribution and a second data distribution.
The calculation module 406: is configured to calculate a first distance between the first data distribution and the second data distribution.
The determination module 408: the method comprises the steps of determining a first position proportion of the first distance in a set number of first sample value reference distances, determining a second position proportion of the first distance in a set number of second sample value reference distances, and determining a degree of selection of a first scheme and a degree of selection of a second scheme of the evaluation object according to the first position proportion and the second position proportion respectively.
The evaluation module 410: configured to evaluate the first and second solutions based on the degree of selection of the first solution and the degree of selection of the second solution quantity.
In an optional embodiment, the obtaining module is further configured to:
and acquiring the user quantity of the plurality of first schemes and the user quantity of the plurality of second schemes of the evaluation object in the observation period from the log file.
In an alternative embodiment, the conversion module is a ratio of the amount of users per day to the amount of users in the observation period.
In an optional embodiment, the determining module includes:
a first determination unit: configured to generate a set number of sample distributions in a random manner;
a second determination unit: configured to calculate a distance between each sample distribution and the first data distribution resulting in a set number of first sample value reference distances, and to calculate a distance between each sample distribution and the second data distribution resulting in a set number of second sample value reference distances;
a third determination unit: and the selecting degree of the first scheme and the selecting degree of the second scheme of the evaluation object are respectively determined according to the first position proportion and the second position proportion.
In an optional embodiment, the first determining unit is further configured to:
a set number of sample distributions are generated by hadoop clustering.
In an alternative embodiment, the evaluation module comprises:
a fourth evaluation unit: configured to determine a medium-selectivity standard value according to an evaluation object;
a fifth evaluation unit: and comparing the degrees of intermediate selection of the first scheme and the second scheme with the standard value of the degrees of intermediate selection, wherein the scheme closer to the standard value of the degrees of intermediate selection is the scheme of intermediate selection.
There is also provided in an embodiment of the present specification a computing device comprising a memory, a processor, and computer instructions stored on the memory and executable on the processor, the processor implementing the steps of the evaluation method when executing the instructions.
An embodiment of the present specification also provides a computer readable storage medium storing computer instructions which, when executed by a processor, implement the steps of the evaluation method.
The above is an illustrative scheme of a computer-readable storage medium of the present embodiment. It should be noted that the technical solution of the storage medium belongs to the same concept as the technical solution of the above-mentioned automatic testing method, and details that are not described in detail in the technical solution of the storage medium can be referred to the description of the technical solution of the above-mentioned automatic testing method.
The computer instructions comprise computer program code which may be in the form of source code, object code, an executable file or some intermediate form, or the like. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
It should be noted that, for the sake of simplicity, the foregoing method embodiments are described as a series of acts or combinations, but those skilled in the art should understand that the present disclosure is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present disclosure. Further, those skilled in the art should also appreciate that the embodiments described in this specification are preferred embodiments and that acts and modules referred to are not necessarily required for this description.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The preferred embodiments of the present specification disclosed above are intended only to aid in the description of the specification. Alternative embodiments are not exhaustive and do not limit the invention to the precise embodiments described. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the specification and its practical application, to thereby enable others skilled in the art to best understand the specification and its practical application. The specification is limited only by the claims and their full scope and equivalents.
Claims (12)
1. An evaluation method, comprising:
acquiring a plurality of first scheme user quantities and a plurality of second scheme user quantities of an application program or a webpage in an observation period;
converting the user quantities of the plurality of first schemes and the user quantities of the plurality of second schemes according to a set conversion mode to obtain a first data distribution and a second data distribution;
calculating a first distance between the first data distribution and the second data distribution;
determining a first position proportion of the first distance in a set number of first sample value reference distances, determining a second position proportion of the first distance in a set number of second sample value reference distances, and respectively determining a first scheme selection degree and a second scheme selection degree of the application program or the webpage according to the first position proportion and the second position proportion;
evaluating the first scheme and the second scheme based on the degree of selection of the first scheme and the degree of selection of the second scheme;
the intermediate degree of the first scheme and the intermediate degree of the second scheme are obtained by the following steps:
generating a set number of sample distributions in a random manner;
calculating the distance between each sample distribution and the first data distribution to obtain a set number of first sample value reference distances, and calculating the distance between each sample distribution and the second data distribution to obtain a set number of second sample value reference distances;
and respectively determining the selection degree of the first scheme and the selection degree of the second scheme of the application program or the webpage according to the first position proportion and the second position proportion.
2. The method of claim 1, wherein obtaining the plurality of first solutions and the plurality of second solutions of the user amount of the application or the web page in the observation period comprises:
and acquiring the user quantity of the plurality of first schemes and the user quantity of the plurality of second schemes of the application program or the webpage in the observation period from the log file.
3. The method of claim 1, wherein the set transition is a ratio of a daily user amount to a total user amount during the observation period.
4. The method of claim 1, wherein randomly generating a set number of sample distributions comprises:
a set number of sample distributions are generated by hadoop clustering.
5. The method of claim 1, wherein evaluating the first and second solutions based on their degrees of selection comprises:
determining a medium-selectivity standard value according to an application program or a webpage;
and comparing the degrees of intermediate selection of the first scheme and the second scheme with the standard value of the degrees of intermediate selection, wherein the scheme closer to the standard value of the degrees of intermediate selection is the scheme of intermediate selection.
6. An evaluation device, comprising:
an acquisition module: the method comprises the steps of obtaining a plurality of first scheme user quantities and a plurality of second scheme user quantities of an application program or a webpage in an observation period;
a conversion module: the system comprises a plurality of first schemes and a plurality of second schemes, wherein the first data distribution and the second data distribution are obtained by converting the user quantity of the first schemes and the user quantity of the second schemes according to a set conversion mode;
a calculation module: configured to calculate a first distance between the first data distribution and the second data distribution;
a determination module: the method comprises the steps of determining a first position proportion of a first distance in a set number of first sample value reference distances, determining a second position proportion of the first distance in a set number of second sample value reference distances, and respectively determining a first scheme selection degree and a second scheme selection degree of the application program or the webpage according to the first position proportion and the second position proportion;
an evaluation module: configured to evaluate a first solution and a second solution based on the degree of selection of the first solution and the degree of selection of the second solution amount;
wherein the determining module comprises:
a first determination unit: configured to generate a set number of sample distributions in a random manner;
a second determination unit: configured to calculate a distance between each sample distribution and the first data distribution resulting in a set number of first sample value reference distances, and to calculate a distance between each sample distribution and the second data distribution resulting in a set number of second sample value reference distances;
a third determination unit: the position proportion is used as the selection degree of the first scheme and the selection degree of the second scheme quantity of the application program or the webpage.
7. The apparatus of claim 6, wherein the acquisition module is further configured to:
and acquiring the user quantity of the plurality of first schemes and the user quantity of the plurality of second schemes of the application program or the webpage in the observation period from the log file.
8. The apparatus of claim 6, wherein the conversion module is a total ratio of the amount of users per day to the amount of users during the observation period.
9. The apparatus of claim 6, wherein the first determining unit is further configured to:
a set number of sample distributions are generated by hadoop clustering.
10. The apparatus of claim 6, wherein the evaluation module comprises:
a fourth evaluation unit: configured to determine a medium-selectability standard value according to an application program or a webpage;
a fifth evaluation unit: and comparing the degrees of intermediate selection of the first scheme and the second scheme with the standard value of the degrees of intermediate selection, wherein the scheme closer to the standard value of the degrees of intermediate selection is the scheme of intermediate selection.
11. A computing device comprising a memory, a processor, and computer instructions stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of claims 1-5 when executing the instructions.
12. A computer-readable storage medium storing computer instructions, which when executed by a processor, perform the steps of the method of any one of claims 1 to 5.
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